这篇论文用实验告诉你：机器翻译加推理步骤质量更好，但token成本涨三成，做生产部署前得算清楚这笔账。
该研究探讨了基于可验证奖励的强化学习（RLVR）在神经机器翻译（NMT）后训练中的应用。实验发现，在推理阶段保留模型的推理痕迹能显著提升翻译质量，尤其在法律文档翻译中表现突出。但推理过程导致输出token数量增加约30%，需在计算成本与翻译质量间权衡。研究通过系统性地从训练或推理阶段移除推理轨迹，明确了推理痕迹对质量的贡献主要来自推理阶段而非训练阶段。
The Price of Reasoning: Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation
Reinforcement learning with verifiable rewards (RLVR) has been established as a viable paradigm for the post-training of Large Language Models (LLMs), including downstream tasks, such as Neural Machine Translation (NMT). With the latest research indicating that RLVR could be the preferred training method for translating legal documents due to the induced reasoning capabilities, it raises the question whether it is really attributed to the reasoning or more generally to the training paradigm. We investigate the importance of including the model's reasoning trace in the generated responses during both training and inference by systematically omitting it from one of the phases. Our experiments show that including the reasoning, specifically during inference, has a positive effect on the overall translation quality. Furthermore, we recognise that the reasoning leads to an increase in output tokens, hence we study the cost-quality tradeoff between the increased computational demands and the improved translation quality.